{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b146d6d3-e2d7-4c2a-acc2-d50a7d0776ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "b = 2\n",
    "print(a +b + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29d19210-3a68-4a4a-b3d5-652b5fa08613",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0e5b0957-ce75-414d-bc67-38f06b3959d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1,2,5,4,3,6]\n",
    "y = [2,3,4,5,6,7]\n",
    "# print(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "82af7830-eb4f-4fb8-8378-a9df86cf32b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "fig1 = plt.figure(figsize=(5,5))\n",
    "plt.plot(x, y)\n",
    "plt.scatter(x, y)\n",
    "plt.title('y vs x')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a22c43e8-dd87-478b-8c40-0622c3851049",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[[1. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.eye(5)\n",
    "print(type(a))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06c734b6-d057-484d-a76a-c70a81f4e38c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
